Confused about autocorrelation and PSD

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SUMMARY

The discussion centers on the relationship between autocorrelation and the Power Spectral Density (PSD) in signal processing. Specifically, it addresses the equation \(\mathsf{E}\left[X(\omega)X^*(\omega')\right]=2\pi\delta(\omega-\omega')S_{XX}(\omega)\), which indicates that the expected value of the Fourier transformed signal is directly proportional to the spectral density function \(S_{XX}(\omega)\). The spectral density is defined as \(S_{XX}(\omega)=\int_{-\infty}^{\infty}r_{XX}(\tau)e^{-i\omega\tau}\,d\tau\). The discussion references the Wiener-Khintchine theorem as a foundational concept for this relationship.

PREREQUISITES
  • Understanding of Fourier transforms
  • Knowledge of autocorrelation functions
  • Familiarity with Power Spectral Density (PSD)
  • Basic principles of the Wiener-Khintchine theorem
NEXT STEPS
  • Study the Wiener-Khintchine theorem in detail
  • Explore the derivation of the relationship between autocorrelation and PSD
  • Learn about the implications of spectral density in signal processing
  • Investigate applications of Fourier transforms in analyzing signals
USEFUL FOR

Signal processing engineers, researchers in communications, and students studying time series analysis will benefit from this discussion.

xaratustra
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I am confused a bit :confused:. I read in a paper that the following property holds, but can't find where it comes from.

\mathsf{E}\left[X(\omega)X^*(\omega')\right]=2\pi\delta(\omega-\omega')S_{XX}(\omega)

it says that the expected value of the Fourier transformed signal is proportional to the spectral density function (PSD) S_{XX}(\omega) which is as usual defined as:

S_{XX}(\omega)=\int_{-\infty}^{\infty}r_{XX}(\tau)e^{-i\omega\tau}\,d\tau

Any one knows where this comes from?

thanks.
 
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